The user experience provided by content discovery applications may depend on the applications' ability to quickly make personalized recommendations relevant to a user's interest. If a user expresses interest in particular content, a content discovery application should be able to react and make high-quality recommendations relevant to the particular content in which the user has expressed interest. If the content discovery application provides irrelevant recommendations to a user, the user experience may be negatively impacted and the user may ignore the recommendations.
The relevance or quality of recommendations provided by the content discovery application can be improved by increasing the complexity of algorithms used to make recommendations. However, increased algorithm complexity may increase the time and computing resources needed to make recommendations. This increased time may cause the user to lose interest in the application for which the recommendation is provided.
The amount of content being considered for potential recommendations and the amount of users handled by the content discovery application compounds the above problems. In addition, miscategorized or inconsistently categorized data may further compound these problems. For example, human users may save various unrelated images to a collection. For example, to save time, by mistake, or due to unfamiliarity, a human user may “save” an image of a classic car to a collection intended for recipes. The association of the car with a recipe collection may lead to the car being recommended to another user that has expressed interest in recipes. Such a recommendation may annoy the user or cause the user to ignore the recommendation. The complexity of the recommendation algorithm may be increased in order to reduce the likelihood that the image of the car will be provided as a recommendation result for a user that has expressed interest in recipes. However, the increased complexity of the algorithm may cause an increased time delay between a user expressing interest in recipes and a recommendation result being provided to the user. This delay may negatively impact the user experience.
In addition, maintaining and generating recommendations from inconsistent or erroneously categorized data may require increased computing resources (e.g., processing and memory). These problems may be compounded for large amounts of human categorized data, e.g., 1+ billion human categorized representations.
Systems and methods are desired that facilitate high-quality relevant recommendations to be provided from a large pool of human-categorized content to many users with minimal delay.